AI in Finance Summit New York schedule

Harry Mendell is a computer scientist/inventor. He invented the first digital sampling synthesizer and collaborated with Stevie Wonder among others. His university thesis was on computer vision and then joined the team at Bell Labs designing the first Unix and microprocessor-based workstation, developing the first memory management co-processor.
He then joined the financial sector, creating algorithms for trading options and managing risk. Following that developed algorithms for trading with alternative data including social media that use machine learning and natural language processing. In 2017 Harry joined the Innovation Group at the New York Federal Reserve applying natural language processing and machine learning to bank supervision.
Currently, Harry is investigating the use of Large Language Models to advance the effectiveness of natural language processing and to meet the needs of the Federal Reserve.

For all the hype and promise of AI, financial services and other industries continue to see tremendous attrition between lifecycle stages from early AI ideation to model development to profitable deployment. Great ideas fall flat or get defunded, team volatility is endemic in high tech, fintech and “tech fin” companies alike and valid concerns about fairness and bias are driving regulatory focus on consumer backlash. This presentation will introduce the essential practice of “cognitive courtesy” with specific applications in product design, explainability and ModelOps, provide key lessons for user-centric design from the world of consumer electronics and equip you with strategies for attracting and retaining talent in challenging times.

-    The importance of “cognitive courtesy” to enable team alignment and create AI solutions that holistically work for end users
-    New ways to attract and retain AI talent
-    How to deliver MVPs when full model lifecycles can span quarters or years

Bjorn Austraat brings more than two decades of diverse experience in taking complex business problems and finding pragmatic, profitable solutions to them through machine learning, AI and other technologies.
Currently, he serves as SVP and Head of AI Acceleration at Truist where he is building out the new AI & Analytics Accelerator (A3) and AI COE to enable innovation and accelerate scalable solution deployment for AI and analytics across the enterprise.
Formerly, Bjorn was SVP for Agile AI at Wells Fargo, Global Cognitive Finance Leader with IBM for a top-3 International Bank and the Global Leader for Cognitive Visioning and Strategy for IBM Watson, where he provided strategic direction for marquee engagements including H&R Block and Vodafone.
Prior to joining IBM, Bjorn held a number of senior leadership roles in companies ranging from Silicon Valley startups to large, multinational consulting enterprises working with companies such as Apple, AT&T, Microsoft and Ford

- What new opportunities do foundation models unlock, and What are the challenges to leveraging them in the enterprise?
- How can a data-centric approach be used to understand, adapt, and distill foundation models?
- What is the roadmap for enterprises to unlock real world value with foundation models?

Foundation models (also known as Large Language Models or LLMs) are accelerating AI in exciting and very visible ways. Any business leader or technologist who has played with ChatGPT is asking how they can put foundation models to work to create value. However, enterprise adoption of foundation models is challenging for a variety of technical and business reasons, including expense of deployment and understanding of behavior. These large models are pushing the boundaries of AI primarily thanks to innovation in the ways the datasets they are trained on have been created, rather than through new developments in model architectures. And similarly, the key to unlocking their value for the enterprise lies in the data. Even in the age of foundation models, data is how you understand a model's behavior in your domain, adapt it to your specific objectives, and translate its performance to downstream applications. In this talk, we explore the connections between data-centric approaches to AI and foundation models, and practical techniques for evaluating, adapting, and leveraging foundation models to deliver real-world value.

The capability of AI continues to mature rapidly and financial services organizations are gaining competency. As companies look to increase their value, AI technologies such as machine learning can help optimize processes, drive new revenue and differentiate your offering. Diana will share insight into how AI/ML can become a key enabler for delivering on your strategy, provide examples of how working closely with the business can reveal transformative AI solutions, as well as provide strategies for retaining and attracting diverse talent.

-    Putting clients at the center of everything you do is critical when developing AI solutions. Businesses should look at AI as a tool to differentiate your product and provide new value to your customers – whether its’ a competitive edge, reduced risk, lower costs, etc.
-    It is important to work in partnership with the business if you want to deliver high impact solutions. It is critical you understand their needs and deploy solutions that can be commercialized. 
-    Industry-wide we need to increase our focus on recruiting and developing diverse talent. Initiatives like Women in AI creates a more diverse work culture and promotes the development of inclusive AI.

Diana serves as a Business Engagement Lead in the Advanced Solutions team within BNY Mellon and has more than 10 years of experience in the areas of strategy development, strategic initiative execution and technology prioritization. 

As a Business Engagement Lead, she is responsible for promoting the use of data science and artificial intelligence capabilities throughout the organization. In this role, she serves as an internal consultant to senior leadership and key stakeholders within the business to uncover business needs and propose solutions utilizing advanced digital solutions that will drive business growth, optimize operational processes, and improve the client experience.

Diana is passionate about developing female talent and is the co-chair of BNY Mellon’s Women in AI initiative, which aims to provides women working and interested in AI a forum to connect, learn and build confidence.

The next frontier in data and AI will bring about significant advancements in processing and analyzing data, with more sophisticated algorithms for natural language processing, image and speech recognition, and machine learning. There will also be a greater focus on the ethical and social implications of AI, including the impact of automation on jobs and the need for greater transparency and accountability in AI decision-making. Additionally, specialized hardware and software platforms for running AI models, such as specialized AI chips, quantum computing, and cloud-based AI services, will become more advanced and widely available.

In the financial services industry, these trends have the potential to transform the customer experience, risk management, and operational efficiency. AI and data analytics can be leveraged to provide personalized banking experiences and enhance fraud detection, leading to more tailored and secure financial products and services. Predictive analytics can help identify potential risks and recommend proactive risk management strategies. Automation can streamline manual processes and reduce operational costs, while blockchain-based solutions can improve transparency and security in areas such as payments and trade finance. These trends are expected to continue to shape the future of the financial services industry, driving innovation and improving customer satisfaction.

- What is the next chapter in the usage of Data in AI?    
- What is the next chapter in AI with respect to the framework of models, computational infrastructure, and social implications?
- How do these new trends in Data and AI improve customer experience, risk management, and operational efficiency in the financial services industry?

Developing scalable and sustainable AI capabilities is no longer a nice-to-have advantage, but a must-to-have competency for companies to survive and thrive in the fast-evolving economy and meet ever-changing consumer expectations.  Building AI capabilities successfully, from the ground up requires not only a deep understanding of the math and technologies, but also developing broad alignment with business strategy, stakeholders, and processes.  In this session, Nan Li will share how to approach AI use cases and develop AI capabilities holistically and strategically.  You will also learn about the common pitfalls to avoid.

- How can AI be leveraged to improve business strategy and production?
- What challenges may need to be considered and how can you reduce the impacts of such?
- What are the building blocks of a successful AI adoption to ensure that the strategy is holistic and achievable?

Nan Li is the VP of AI/ML and Statistical Practice at Nationwide in Columbus, Ohio. Nationwide is one of the largest and strongest diversified insurance and financial services organizations in the United States. Nan is a passionate, versatile, and human-centric data and analytics executive with over 20 years of experience in the insurance, financial services, and healthcare industries. A creative and pragmatic business problem solver, innovator, and communicator, Nan is skilled at setting up data & analytics strategy and roadmap, bringing business, analytics, and IT together to achieve business outcomes and operationalizing data & analytics solutions to deliver scalability and ROI.

- Exploring the current opportunities and challenges in terms of capturing ROI when developing and deploying AI within BFSI
- How can possible areas for improvement in AI pipelines be identified?
- How can we optimize engineering, infrastructure, culture, teams and decision-making to see increased ROI (higher revenue, cost reduction, operational efficiency, value gains)?

Harry Mendell is a computer scientist/inventor. He invented the first digital sampling synthesizer and collaborated with Stevie Wonder among others. His university thesis was on computer vision and then joined the team at Bell Labs designing the first Unix and microprocessor-based workstation, developing the first memory management co-processor.

He then joined the financial sector, creating algorithms for trading options and managing risk. Following that developed algorithms for trading with alternative data including social media that use machine learning and natural language processing. In 2017 Harry joined the Innovation Group at the New York Federal Reserve applying natural language processing and machine learning to bank supervision.

Currently, Harry is investigating the use of Large Language Models to advance the effectiveness of natural language processing and to meet the needs of the Federal Reserve.

Ercan Ucak is a Vice President within the Tech Strategy group of Cerberus Technology Solutions ("CTS"). Mr Ucak began his career as a Data Scientist in the Defense / Counter-intelligence and eventually Commercial space, where he leveraged his background in Statistics to develop ML / AI models to enable predictive analytics. He eventually joined CTS on Nov.19, utilizing his deep technical expertise to deploy Data & Analytics tools to create Enterprise Value.

In this session, you’ll get a high-level overview of how to scale AI in your organization, team, or department. You’ll learn about fueling innovation at all levels, evaluating and prioritizing AI investments, establishing a path for innovation, and distributing AI-related responsibilities across the organization.
You will learn how to focus on creating value instead of thinking solely about technology when it comes to innovation. We will discuss criteria for innovation, innovative culture, innovation centers, innovative leadership styles, and different types of innovation.

- Identifying the use cases and business problems that are best suited for AI solutions, and how to prioritize them
- Developing an AI strategy and roadmap that aligns with the organization's overall business objectives
- Building a team and infrastructure to support the implementation and scaling of AI
- Addressing the technical and organizational challenges that arise when scaling AI from innovation centres to enterprise-wide solutions, such as data governance and security

Olga is a passionate AI/ML leader. She has been recognized as top 25 women in AI in Canada and top 100 women globally advancing AI in 2023 by Re:Work. She has spent the last 15 years in various senior roles in technology, specifically in data science, big data, data engineering, analytics, and data warehousing. She is a Director of Advanced Analytics and Data Science at the Royal Bank of Canada. Olga brings data to life through machine learning, analytics, and visualization. Outside of her work at RBC, she has worked directly with global organizations such as the UN Environment World Conservation Monitoring Centre, World Resources Institute, and prominent Canadian non-profits such as War Child Canada and Rainbow Railroad on various data science and analytics challenges.

Many firms have turned to AI solutions in order to streamline their processes, improve their forecasting abilities and align themselves with increasingly stringent regulatory frameworks. The release of ChatGPT by OpenAI in December 2022 has drawn an incredible amount of attention towards the capabilities of LLMs. In this session, we will walk you through real-world use cases to improve knowledge management and operational efficiencies.

- Understanding the capabilities of LLMs and their potential to drive value for businesses
- Exploring real-world use cases of LLMs to improve knowledge management and operational efficiencies
- Understanding the opportunities and challenges of leveraging LLMs in various industries, including finance

Claire leads the Business Transformation practice at Dataiku, which helps customers accelerate their transformation thanks to AI. She is a seasoned leader who spent most of her career in management consulting, advising large organizations on their digital transformation, and at PayPal, where she was leading the peer-to-peer payments product line notably during the mobile revolution. Claire is passionate about how large organizations, but also each individual, embrace change and transform their way of working and making decisions thanks to technology and data. She has traveled the world and what she loves the most about her job is connecting the dots and sharing best practices across multiple geographies and industries.

Recent advances in machine learning and artificial intelligence suggest the emergence of a remarkable class of models that can learn a general representation of data to give rise to many downstream tasks. A general representation is the automatic transformation of data into a rich, abstract, reusable, and high-dimensional knowledge graph, which can be used to build many applications on top of, much more efficiently than building a specialized model from scratch for a single application. In this talk, I will describe how to design such models, why they work well (scaling law of deep learning) and how they could give rise to next generation of Al-enabled financial systems.

- What are Generalists AI Systems and why should they be considered?
- How are these models designed and implemented?
- How could they could give rise to next generation of Al-enabled financial systems?

Ramin Hasani is a Principal AI and Machine Learning Scientist at the Vanguard Group and a Research Affiliate at the Computer Science and Artificial Intelligence Lab (CSAIL), Massachusetts Institute of Technology (MIT). Ramin’s research focuses on robust deep learning and decision-making in complex dynamical systems. Previously he was a Postdoctoral Associate at CSAIL MIT, leading research on modeling intelligence and sequential decision-making, with Prof. Daniela Rus. He received his Ph.D. degree with distinction in Computer Science at Vienna University of Technology (TU Wien), Austria (May 2020). His Ph.D. dissertation and continued research on Liquid Neural Networks got recognized internationally with numerous nominations and awards such as TÜV Austria Dissertation Award nomination in 2020, and HPC Innovation Excellence Award in 2022. He has also been a frequent TEDx Speaker. 

Bayesian optimization is an approach for optimizing objective functions that are expensive to evaluate. “Vanilla” Bayesian optimization is typically best-suited for optimization over continuous domains of less than 20 dimensions and is tolerable to noisy function evaluations. It works by fitting a surrogate model to the objective function and quantifying uncertainty in the surrogate using a Bayesian machine learning technique, Gaussian process regression; we then build an acquisition function from this surrogate to decide where to sample. Although not limited to hyperparameter tuning, we will discuss how Bayesian Optimization is used to find optimal hyperparameters for machine learning models.

- What is Bayesian Optimization and how does it work?
- How can Bayesian Optimization be used to find optimal hyperparameters for machine learning models?
- What impact does this have within the financial sector?

A PhD candidate in Cornell University’s computer science department, Darian Nwankwo—an Atlanta, GA, native—is an enthusiastic problem-solver dedicated to applying his computational and mathematical skills to problems in various domains. Before beginning his matriculation at Cornell, he graduated top of his class from Morehouse College’s computer science department, earning membership into Phi Beta Kappa Honor Society along the way.
He has several industry and academic positions that have contributed to his diverse perspective on problem-solving. He was initially exposed to industry standards on how to write software while at Google. Subsequently, he decided to pursue academic research where he studied Human-Computer Interaction at Stanford university and Mathematical Biology at Morehouse College. After gaining exposure to work in industry and academia, he joined an industry research lab at Adobe, prior to starting his graduate studies.
His academic research afforded him a position with industry titan IBM where he worked with their Analog AI research team helping develop next generation hardware for accelerating AI applications. Upon completion, he was recognized by some researchers at AMD where he worked as a Scientific Machine Learning researcher, helping to advance our understanding on developing heterogenous systems for machine learning workloads.
In his spare time, Darian enjoys reading, learning new mathematics, playing pool and exercising. He is currently going through a curriculum on Quantitative Finance to learn how mathematics and computer science is leveraged in other disciplines to whet his curiosity. 

- Discover the importance of aligning your AI strategy with your overall business goals and how to identify which areas of your business could benefit most from AI implementation.
- What is the range of AI technologies available today and how to evaluate which ones are best suited for your specific use case
- Discuss the need for flexibility and adaptability in your AI strategy, including how to adjust your approach in response to changing market conditions, emerging technologies, and evolving customer needs.

Upal is a Product Owner at Fidelity Investments responsible for creating AI driven recommendation solutions that identifies a customer’s financial needs and deliver relevant experiences to help them find products and solutions aligned to these needs. 
In his role as a Product Owner, Upal leads a cross-functional team of data scientists, data engineers and analysts who helps create, deploy and measure the impact of these AI solutions. 
Upal brings over 12 years of experience in solving complex business problems through analytics, technology and data solutions. He is passionate about delivering measurable customer value through scalable AI capabilities.

Supreet is an AVP at Morgan Stanley. Prior to Morgan Stanley, she was a management consultant at ZS Associates where she automated different workflows and built data driven solutions for fortune 500 clients. She is extremely passionate about technology and AI and hence started her own community called DataBuzz where she engages the audience by sharing the latest AI and Tech trends and also mentors people who want to pivot in this field

Martin Ouko currently leads TIAA’s Model Lifestyle Management & Execution organization consisting of a highly skilled global team of Data, Pipeline and Model-Ops Engineers, tasked with designing, developing and productionizing elegant technology integrations and orchestrations that solve business problems and challenges of varying complexities. 
While not tinkering with cutting edge technology, he moonlights as Adjunct Professor, training future business leaders on subjects matters ranging from Economics to Business Management Concepts at a local college.
Outside industry, technology and academia, he serves on the board of Kiwimbi International, a Non-Profit organization dedicated to fundamentally altering the literacy culture in underserved communities around the world; one kid at a time.
In between these engagements, he can be found in the trees, sand bunkers or the rough, looking for his golf balls. And when he can’t find the fairways consistently, he prefers bringing together Charitable Organizations and philanthropists to bond over a good game under the banner of Prestige Invitational.

Alejandro Zarate Santovena has more than 25 years of global experience in technology, consulting, and marketing in Europe, Latin America and the US. Through his work, Alejandro has become a leader in business intelligence, product development, Artificial Intelligence, and team leadership.
Alejandro began working in insurance sales and business development in 2010 at Marsh Mexico. He is now Managing Director at Marsh-USA where he leads Data Strategy, focused on leading the development of Machine Learning and Data Science applications to drive business intelligence and innovative product development globally. Alejandro leads teams in New York, London and Dublin.

As an early member of Snorkel AI’s go-to-market team, Karl has helped multiple Fortune 500 companies solve intractable problems using data-centric AI. Prior to Snorkel, he helped enterprises transform their businesses with disruptive technology in roles at AWS, MuleSoft, and Salesforce. Karl is based in New York and holds an AB in History from Dartmouth College. Before getting into tech, he lived in the Korean countryside for a year and taught English in a small garlic town named Uiseong. 

- Can blockchain platforms offer an alternative to platform governance?
- How could such platforms impact decision making processes?
- How would this then impact the regulation of such decisions?

George Samakovitis is Professor of FinTech and Deputy Head of School of Computing & Mathematical Sciences at the University of Greenwich, UK. George specialises in banking and payment systems technologies, Enterprise Architectures and AI for FinTech. His present research focuses on the deployment and governance of technologies for Anti-Money Laundering and Financial Crime, with particular emphasis on the use of DLT agents and development of blockchain infrastructure to deliver Collective Intelligence capabilities in FinTech networks.

George is presently a member of the Counter Fraud & Data Analytics Advisory Group of the HMG Cabinet Office and has served as a member of the FinCrime Working Group at the UK Payments Strategy Forum (2015-18), particularly working on KYC and Transaction Data Sharing and Analytics strategies and solutions for UK Financial Services. Most recently, he joined the BSI UK Data Standards Expert Panel, a diverse cross-sector panel of senior data executives, aiming to coordinate data standards interoperability across UK industry sectors.

George’s past work focused, among other, on banking technology investment decisions in economic booms and downturns, addressing, among other issues, the banking sector’s attitudes to uncertainty and risk under the disparate decision-making paradigms dictated by economic climate.

Large enterprise organizations certainly benefit from economies of scale. However, with greater size, operational bottlenecks in the handling of data are ever present for security, regulatory compliance and structural reasons. The tradeoff that most enterprises are forced to make is sacrificing innovation to avoid risk, which of course is problematic. In this talk, I will explain how to use synthetic data and generative AI for structured data to mitigate risk while simultaneously increase business agility and drive tangible business outcomes. 

- Why is structured data important for financial decision making and what are the common challenges associated with using it?
- How can generative AI be used to generate synthetic data to supplement limited data sets in finance?
- How can generative AI improve the performance of machine learning models?

Nicolai Baldin is Chief Executive Officer and Founder of Synthesized. Synthesized provides high-quality data for machine learning and application development. it solves the problem of replacing expensive sensitive production data from dev environments with right-sized synthetically generated data. Nicolai is responsible for the direction and global strategy of Synthesized. He holds a PhD in Statistics and Machine Learning from the University of Cambridge where he won the prestigious Smith-Knight award and is a Forbes Technology Council member.

Today, ChatGPT, BARD and numerous Generative AI services gained popularity rapidly due to their impressive linguistic capability and high quality context-aware response.  This technology changes how people think of AI.  In Financial Sector, what does this technology advancement mean to us?  How does it change the way we think and work?  What do we need to consider when adopting and implementing this technology?  This session will explore the capabilities such as generating synthetic data and use it on document understanding and against fraud, and highlight the latest trends using Generative AI.

- Learn about the latest developments in generative AI and its applications in the banking sector
- Understand how generative AI can be used to improve financial forecasting, risk assessment and decision-making in banking
- Discover how generative AI can be used to create new financial products and services such as synthetic data, virtual assistants and personalization

John Chan is a Director of Technology at Raymond James Financial running the Carillon Labs - the innovation labs specialized in AI/ML.  His passion is to promote AI adoptions and implement machine learning solutions in Financial Sector.  He has 20+ years experience leading and implementing technology solutions from FinTech startups to top-tier banks and consulting firms.  Prior to Raymond James, John was an AI strategist and engineering lead at Gamma Lab of OneConnect Financial, Morgan Stanley data science team and KPMG Cognitive Technology Lab.  He is active in NLP research focusing on Generative AI, Conversational AI, Document Understanding and risk and compliance technology.

Financial literature consists of ample research on similarity and comparison of financial assets and securities such as stocks, bonds, mutual funds, etc. However, going beyond correlations or aggregate statistics has been arduous since financial datasets are noisy, lack useful features, have missing data and often lack ground truth or annotated labels. Though similarity extrapolated from these traditional models heuristically may work well on an aggregate level, such as risk management when looking at large portfolios, they often fail when used for portfolio construction and trading which require a local and dynamic measure of similarity on top of global measure. In this talk, I will start by demonstrating the importance of my research program on similarity learning in the financial domain with providing many different potential applications. I will then describe various similarity learning methods with their advantages and disadvantages, and finally focus on distance metric learning applied to corporate bond similarity as an application.

- Importance of similarity learning in finance
- Rigorous definition of similarity (supervised similarity) is crucial
- Introduction of tree-based distance metric learning
- Real example of supervised similarity for corporate bonds to identify liquid substitutes

Dr. Mehta is the Head of Applied Machine Learning Research (Investment Management) at Blackrock Inc. and an Editorial Board Member at the Journal of Financial Data Science.

Previously he was a Senior Manager, Investment Strategist (Machine Learning – Asset Allocation) at Investment Strategy Group at The Vanguard Group. Before joining Vanguard, he was a Senior Research Scientist at United Technologies (UTX) Research Center. Prior to that, he was a Research Assistant Professor at the Department of Applied and Computational Mathematics and Statistics in conjunction with the Department of Chemical and Biomolecular Engineering at the University of Notre Dame. He was a Fields Institute Postdoc Fellow for the Thematic Program on Computer Algebra at Fields Institute, Toronto, in the Fall of 2015 and a Visiting Fellow at Simons Institute for Theory of Computing at Berkeley in the Fall of 2014. Previously, he has held various research positions at the University of Cambridge (the UK), Imperial College London (the UK), the University of Adelaide (Australia), Syracuse University (USA) and the National University of Ireland Maynooth (Ireland).
Dr Mehta’s research areas are machine/deep learning; quantitative finance, computational mathematics, science and engineering. In particular, I work on optimization (convex and nonconvex), computational algebraic geometry, numerical analysis, network science and machine learning to solve various problems arising in financial services and wealth/asset management (and in the past, power systems and control theory; and theoretical and computational physics, jet-engines, HVAC and building systems, chemistry and biology).

Comprehending natural language text with its first-hand challenges of ambiguity, synonymity, and co-reference has been a long-standing problem in Natural Language Processing (NLP). The domain of NLP has seen a tremendous amount of research and innovation in terms of the new Transformer-based large language models (LLM). This AI capability is quickly getting its foothold in the finance industry. This technology has enabled incredible breakthroughs in performance in various financial tasks using unstructured text like classification, topic modeling, and semantic search being the top requested capabilities. This is because the LLMs are trained on a broader corpus of data providing a much-needed contextual depth to support complex use cases vs smaller models trained with a few sample data points. Question-Answering is one such area that is crucial in finance to explore large text datasets and find insights quickly via Information Retrieval.
This talk will highlight the general concepts and ways of implementing the large language model to build an efficient question-answering model. This also ensures that by using the available open-source platforms we are able to have better business outputs as well as a better environment because training a single AI model contributes to 5 cars' lifetime worth of carbon emissions. Code can be made available via GitHub for everyone to examine after the talk.

- Review the current NLP trends and latest SOTA algorithms Overview of the latest NLP algorithms and industry use cases that are easier to solve using the open-source NLP methods

- Deep-dive into Transformers and LLM-based architecture Understand the model architecture and its workings and why it’s a massive improvement over previous language models. Explore the problem statement and steps to solve it.

- Walkthrough of the Code: Colab notebook walkthrough to go step by step through the process of how to use open-source NLP large language models (LLMs) to build a domain-specific Question-Answering model.

Jayeeta is a Senior Data Scientist with several years of industry experience in Natural Language Processing (NLP), Statistical Modeling, Product Analytics and implementing ML solutions for specialized use cases. Currently, Jayeeta works at Fitch Ratings, New York, a global leader in financial information services. She is an avid NLP researcher and gets to explore a lot of state-of-the-art open-source models to build impactful products and firmly believes that data, of all forms, is the best storyteller.

Jayeeta also led multiple NLP workshops in association with Women Who Code, and GitNation among others. Jayeeta has also been invited to speak at International Conference on Machine Learning (ICML 2022), ODSC East, MLConf EU, WomenTech Global Conference, Data Science Salon, and Data Summit Connect, to name a few. She is also an ambassador for Women in Data Science, at Stanford University, and a Data Science Mentor at Girl Up, United Nations Foundation, and WomenTech Network where she aims to inspire more women to take up STEM.

Jayeeta has been nominated for the WomenTech Global Awards 2020 and has been spotlighted in the List of Top 100 Women Who Break the Bias 2022. She received her MS in Quantitative Methods and Modeling and a BS in Economics and Statistics and is now based in New York City.

- Overview of the current landscape of card and payments fraud, including common types of fraud and their impact on businesses and consumers
- Discussion of the potential of machine learning and other AI-based techniques for detecting and preventing fraud
- Examination of the challenges and limitations of using machine learning for fraud detection and ways to overcome them
- Explore Use-Cases of how banks and financial services are combatting card and payment fraud

- Why is it important for the financial risk sector to remain on top of technological advancements and trends? 
- How can AI/ML be utilised to both detect and prevent fraudulent activity? 
- What advancements in this space can we expect to see? 

Sateesh Kumar Challa is an accomplished leader with 18 years of experience in digital transformation. As the Head of the Digital Transformation Office at Societe Generale, Sateesh is responsible for leading the company's efforts to drive innovation and digital change across the Risk & Compliance organization.
Throughout his career, Sateesh has successfully led cross-functional teams, developed and executed digital transformation strategies, and delivered successful projects that have transformed organizations.

Sateesh has extensive experience in digital transformation, with expertise in emerging technologies, Digital strategy, model development life cycle, software development life cycle, data modeling, governance, and operational efficiency.

Before joining Societe Generale, Sateesh held leadership positions in several organizations where they contributed significantly to their digital transformation initiatives. Sateesh is a well-known thought leader in the industry, focused on practical insights and actionable advice. He has spoken at numerous industry events and conferences on digital transformation, AI/ML, Data, emerging technologies, and innovation topics.

Sateesh volunteers to support various causes on economic empowerment and the environment. He holds an Executive MBA, a Master of Computer Applications degree, and a Project Management Certification.

-    How can you level up your existing anti-fraud systems?
-    What are the latest advancements of AI in the fraud detection industry?
-    What to expect in the coming years? 

Data Scientist at The Hartford and Teaching Associate for Master's students at Columbia University.

Sateesh Kumar Challa is an accomplished leader with 18 years of experience in digital transformation. As the Head of the Digital Transformation Office at Societe Generale, Sateesh is responsible for leading the company's efforts to drive innovation and digital change across the Risk & Compliance organization.
Throughout his career, Sateesh has successfully led cross-functional teams, developed and executed digital transformation strategies, and delivered successful projects that have transformed organizations.

Sateesh has extensive experience in digital transformation, with expertise in emerging technologies, Digital strategy, model development life cycle, software development life cycle, data modeling, governance, and operational efficiency.

Before joining Societe Generale, Sateesh held leadership positions in several organizations where they contributed significantly to their digital transformation initiatives. Sateesh is a well-known thought leader in the industry, focused on practical insights and actionable advice. He has spoken at numerous industry events and conferences on digital transformation, AI/ML, Data, emerging technologies, and innovation topics.

Sateesh volunteers to support various causes on economic empowerment and the environment. He holds an Executive MBA, a Master of Computer Applications degree, and a Project Management Certification.